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LLMs & generative AIDetection methods TELLER: A Trustworthy Framework For Explainable, Generalizable and Controllable Fake News DetectionTELLER proposes a dual-system framework for fake news detection that combines LLM-driven cognition (decomposing content into yes/no questions) with a neural-symbolic decision system (learning interpretable rules). The approach prioritizes explainability, generalizability, and human controllability, achieving 76% accuracy on GossipCop and… Liu et al. · arXiv · 2024

LLMs & generative AIDatasets & evaluation Advancing Graph Representation Learning with Large Language Models: A Comprehensive Survey of TechniquesThis comprehensive survey analyzes the integration of Large Language Models (LLMs) with Graph Representation Learning (GRL), breaking down the design space into two primary components—knowledge extractors and organizers—plus operation techniques for integration and training. The work provides a novel taxonomy for understanding how LLMs… Mao et al. · arXiv · 2024

LLMs & generative AIDetection methods RAIDAR: Generative AI Detection via RewritingThis paper introduces Raidar, a method to detect machine-generated text by measuring how much LLMs modify text when asked to rewrite it. The key observation is that LLMs tend to preserve AI-generated text and modify human-written text more when rewriting. Mao et al. · ICLR · 2024

LLMs & generative AIPlatforms, policy & society Generative AI Misuse: A Taxonomy of Tactics and Insights from Real-World DataThis paper presents the first comprehensive taxonomy of generative AI misuse tactics grounded in 191 real-world documented incidents (January 2023–March 2024). The authors identify two broad categories—exploitation of GenAI capabilities and compromise of GenAI systems—with 18 distinct tactics. Marchal et al. · arXiv · 2024

LLMs & generative AIDetection methods DELL: Generating Reactions and Explanations for LLM-Based Misinformation DetectionLarge language models struggle with factuality and hallucinations in misinformation detection. DELL proposes a three-stage pipeline where LLMs generate synthetic user reactions from diverse perspectives, create explanations for proxy tasks (sentiment, framing, propaganda tactics), and merge predictions from task-specific experts. Wan et al. · arXiv · 2024

LLMs & generative AIDetection methods Can LLM-Generated Misinformation Be Detected?LLM-generated misinformation (from ChatGPT, Llama, Vicuna) is detectably harder for both humans and automated detectors to identify than human-written misinformation with identical semantics. The paper builds a taxonomy of LLM-generated misinformation across types, domains, and generation methods, then empirically demonstrates that… Chen & Shu · ICLR 2024 · 2024

LLMs & generative AI A Survey of Large Language ModelsThis comprehensive survey reviews the evolution of large language models (LLMs) from statistical language models through pre-trained models to contemporary transformer-based LLMs. The authors examine four major dimensions: pre-training strategies, parameter-efficient adaptation methods, utilization approaches (prompting and in-context… Zhao et al. · arXiv · 2023

LLMs & generative AIDetection methods Self-RAG: Learning to Retrieve, Generate, and Critique through Self-ReflectionThis paper introduces SELF-RAG (Self-Reflective Retrieval-Augmented Generation), a framework that teaches language models to adaptively decide when to retrieve external passages, generate factually grounded responses, and critique their own outputs. Using special "reflection tokens," the model learns to self-evaluate at inference time… Asai et al. · arXiv · 2023

LLMs & generative AIDatasets & evaluation The Looming Threat of Fake and LLM-generated LinkedIn Profiles: Challenges and Opportunities for Detection and PreventionLinkedIn's lack of verification enables fake accounts that harm legitimate users and the platform's integrity. This work introduces a dataset of 3600 profiles (legitimate, manually created fake, and ChatGPT-generated) and proposes SSTE, a method that detects fake profiles immediately after registration using only profile text. Ayoobi et al. · 3rd ACM Conference on Hypertext and Social Media · 2023

LLMs & generative AIDeepfakes & multimodal A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPTThis is the first comprehensive survey of AI-Generated Content (AIGC) covering the history, techniques, and applications of generative models from GANs through ChatGPT. It addresses both the impressive capabilities of these models (text, image, audio, video generation) and critical concerns including factuality, security, privacy, and… Cao et al. · J. ACM, Vol. 37, No. 4 · 2023

LLMs & generative AIDatasets & evaluation A Survey on Evaluation of Large Language ModelsThis survey provides a comprehensive review of evaluation methodologies for large language models across three dimensions: what to evaluate, where to evaluate, and how to evaluate. The paper encompasses evaluation tasks spanning natural language processing, natural language generation, robustness, ethics, bias detection, trustworthiness,… Chang et al. · Journal of the ACM · 2023

LLMs & generative AIDetection methods A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and ExplainabilityA comprehensive survey of trustworthy Graph Neural Networks covering four critical dimensions: privacy attacks and defenses against membership/property inference and model extraction; robustness against adversarial perturbations; fairness mechanisms to prevent discrimination in graph-based predictions; and explainability methods to… Dai et al. · ACM Computing Surveys · 2023

LLMs & generative AIPlatforms, policy & society Toxicity in ChatGPT: Analyzing Persona-assigned Language ModelsLarge-scale empirical analysis of ChatGPT's toxicity across 90 personas using 500K+ generations. Assigning personas via the system parameter increases toxicity up to 6×, with variation depending on persona characteristics and target entities. Deshpande et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Generative Language Models and Automated Influence Operations: Emerging Threats and Potential MitigationsGenerative language models like GPT-3 and ChatGPT could significantly expand the scale and scope of influence operations by automating high-quality propaganda generation, lowering barriers to entry for new propagandists, and enabling novel tactics. The authors use an Actor-Behavior-Content (ABC) framework to identify how these models… Goldstein et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Trustworthy LLMs: A Survey and Guideline for Evaluating Large Language Models' AlignmentThis survey provides a comprehensive framework for evaluating LLM trustworthiness across seven dimensions: reliability, safety, fairness, resistance to misuse, explainability, social norms, and robustness. The authors present a detailed taxonomy with 29 sub-categories, conduct empirical measurements on widely-used LLMs (GPT-4, ChatGPT,… Liu et al. · arXiv · 2023

Platforms, policy & societyLLMs & generative AI Disinformation 2.0 in the Age of AI: A Cybersecurity PerspectiveDisinformation should be treated as a cybersecurity threat, particularly as AI advances enable its creation and dissemination at unprecedented scale. The paper introduces "disinformation 2.0"—where generative AI, perturbation techniques, detection evasion, and optimized spread strategies make false content more persuasive and harder to… Mazurczyk et al. · ACM Publication · 2023

LLMs & generative AIDeepfakes & multimodal FLIRT: Feedback Loop In-context Red TeamingFLIRT automates red teaming of generative models by using in-context learning to generate adversarial prompts in a feedback loop: a red language model generates candidate prompts, they are evaluated against a target model (e.g., text-to-image or text-to-text), safety classifiers assess the outputs, and results feed back to refine future… Mehrabi et al. · arXiv · 2023

Fact-checking & verificationLLMs & generative AI The Perils & Promises of Fact-checking with Large Language ModelsThis paper evaluates GPT-3.5 and GPT-4's ability to fact-check claims across English-only and multilingual datasets, showing GPT-4 substantially outperforms GPT-3.5 but both models show inconsistent accuracy when dealing with ambiguous verdicts. Critically, incorporating contextual information via Google Search significantly improves… Quelle & Bovet · arXiv · 2023

Spread & networksPlatforms, policy & society The Amplification Paradox in Recommender SystemsAlgorithmic audits find that blindly following YouTube recommendations leads to extreme content, yet real user logs show recommendations don't drive extreme content consumption. Using an agent-based model with realistic user preferences, the authors show this paradox arises because users rarely consume niche content even when recommended… Ribeiro et al. · ICWSM · 2023

LLMs & generative AIDetection methods Adapting Fake News Detection to the Era of Large Language ModelsAs large language models proliferate and machine-generated text becomes prevalent, traditional fake news detectors trained exclusively on human-written content are increasingly unreliable. This paper evaluates fake news detectors across three hypothetical eras—human-written dominance, mixed human-machine content, and machine-generated… Su et al. · arXiv · 2023

LLMs & generative AIDetection methods The Science of Detecting LLM-Generated TextsThis survey comprehensively reviews techniques for detecting LLM-generated text, a critical problem given concerns about misinformation, academic dishonesty, and unauthorized use. The authors categorize detection approaches into black-box (API-level access) and white-box (full model access) methods, discuss watermarking strategies,… Tang et al. · arXiv · 2023

LLMs & generative AIFact-checking & verification Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityThis comprehensive survey addresses the critical problem of factuality in large language models, examining how LLMs store and retrieve knowledge, analyzing the mechanisms through which they generate false or misleading content, and reviewing evaluation metrics and enhancement strategies. The paper distinguishes factuality issues from… Wang et al. · arXiv · 2023

LLMs & generative AIPlatforms, policy & society Jailbroken: How Does LLM Safety Training Fail?This paper investigates why large language models trained for safety remain vulnerable to jailbreak attacks. Through analysis of models including GPT-4 and Claude v1.3, the authors identify two key failure modes: competing objectives (where safety constraints conflict with other capabilities) and mismatched generalization (where safety… Wei et al. · arXiv · 2023

LLMs & generative AIDetection methods A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future DirectionsAs LLMs become ubiquitous, detecting machine-generated text is critical for mitigating risks of misinformation, academic dishonesty, and misuse. This survey comprehensively reviews detection strategies—watermarking techniques, statistical methods, neural-based detectors, and human-assisted approaches—along with datasets, evaluation… Wu et al. · arXiv · 2023